Artificial Intelligence: Advanced Machine Learning

Artificial Intelligence: Advanced Machine Learning, available at $44.99, has an average rating of 3.9, with 48 lectures, based on 41 reviews, and has 529 subscribers.

You will learn about Extract features from categorical variables, text, and images Solve real-world problems using machine learning techniques Exploit the power of Python to handle data extraction, manipulation, and exploration techniques Implement machine learning classification and regression algorithms from scratch in Python Dive deep into the world of analytics to predict situations correctly Predict the values of continuous variables Classify documents and images using logistic regression and support vector machines Create ensembles of estimators using bagging and boosting techniques Evaluate the performance of machine learning systems in common tasks This course is ideal for individuals who are The course is intended for both professionals and students. or Anyone who wants to learn advanced machine learning skills It is particularly useful for The course is intended for both professionals and students. or Anyone who wants to learn advanced machine learning skills.

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Summary

Title: Artificial Intelligence: Advanced Machine Learning

Price: $44.99

Average Rating: 3.9

Number of Lectures: 48

Number of Published Lectures: 47

Number of Curriculum Items: 48

Number of Published Curriculum Objects: 47

Original Price: $189.99

Quality Status: approved

Status: Live

What You Will Learn

Extract features from categorical variables, text, and images
Solve real-world problems using machine learning techniques
Exploit the power of Python to handle data extraction, manipulation, and exploration techniques
Implement machine learning classification and regression algorithms from scratch in Python
Dive deep into the world of analytics to predict situations correctly
Predict the values of continuous variables
Classify documents and images using logistic regression and support vector machines
Create ensembles of estimators using bagging and boosting techniques
Evaluate the performance of machine learning systems in common tasks

Who Should Attend

The course is intended for both professionals and students.
Anyone who wants to learn advanced machine learning skills

Target Audiences

The course is intended for both professionals and students.
Anyone who wants to learn advanced machine learning skills

Unlock the Power of Data Science and Machine Learning with Python: A Comprehensive, Hands-on Journey to Mastering Advanced Algorithms and Techniques

Dive into the captivating realm of data science and machine learning, where cutting-edge algorithms and sophisticated techniques empower you to create intelligent, efficient models that revolutionize the way we analyze data. In this highly immersive course, you’ll harness the versatility and power of Python, the foremost programming language for machine learning, to unlock new dimensions of analytical prowess.

Delve into a rich curriculum that seamlessly integrates hands-on projects, advanced algorithm exploration, and striking visualization techniques to foster a deep understanding of machine learning. Learn to predict insurance types based on patient treatment using Random Forests, then examine and fine-tune your model for optimal performance. Train a program to recognize letters using Support Vector Machines, analyze the results, and create an illuminating confusion matrix.

Throughout this dynamic learning experience, you’ll unravel the mysteries of vital machine learning algorithms, mastering their mechanics and applications. Build innovative systems that classify documents, recognize images, detect ads, and more, all while refining your skills with Scikit-learn’s API to extract features from categorical variables, text, and images. Develop an intuitive grasp of model performance evaluation and the keys to enhancing your model’s effectiveness.

Upon completing this course, you’ll be armed with a comprehensive understanding of machine learning concepts, enabling you to construct powerful models that tackle advanced tasks with ease and precision. Embark on this transformative journey and become a sought-after professional in the rapidly evolving world of data science and machine learning.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome

Chapter 2: Getting Started With This Course

Lecture 1: Set up the environment

Lecture 2: Machine Learning – Classification

Lecture 3: Machine Learning – Regression

Lecture 4: Machine Learning – Transformers

Lecture 5: Machine Learning – Clustering

Lecture 6: Machine Learning – Manifold Learning

Lecture 7: Machine Learning – Scikit-learn's estimator interface

Lecture 8: Machine Learning – Cross-Validation

Lecture 9: Machine Learning – Grid Searches

Chapter 3: Machine Learning – Model Complexity

Lecture 1: Introduction

Lecture 2: Linear models for regression

Lecture 3: Support Vector Machines

Lecture 4: Trees and Forests

Lecture 5: Learning Curves

Lecture 6: Validation Curves

Lecture 7: EstimatorCV Objects for Efficient Parameter Search

Chapter 4: Understanding Pipelines

Lecture 1: Pipelines – Motivation

Lecture 2: Pipeline Baiscs

Lecture 3: Cross Validation With Pipelines

Lecture 4: Using Pipelines with Grid-Search

Chapter 5: Machine Learning – Imbalanced Classes & Metrics

Lecture 1: Default metrics

Lecture 2: Classification Metrics

Lecture 3: Precision – Recall tradeoff and Area Under the Curve

Lecture 4: Built-In and custom scoring functions

Chapter 6: Machine Learning – Model Selection For Unsupervised Learning

Lecture 1: How to evaluate unsupervised models?

Lecture 2: Kernel Density Estimation

Lecture 3: Model Selection For Clustering

Chapter 7: Machine Learning – Handling Real Data

Lecture 1: Dealing with Real Data

Lecture 2: OneHotEncoder

Lecture 3: Encoding Features from Dictionaries

Lecture 4: Handling missing values

Chapter 8: Machine Learning – Dealing with Text Data

Lecture 1: Text Data Motivation

Lecture 2: Text Feature Extraction with Bag-of-Words

Lecture 3: Text Classification of Movie Reviews

Lecture 4: Text Classification continuation

Lecture 5: Text Feature Extraction Hashing Trick

Lecture 6: Vector Representations

Chapter 9: Machine Learning – Out Of Core Learning

Lecture 1: Out of Core and Online Learning

Lecture 2: The Partial Fit Interface

Lecture 3: Kernel Approximations

Lecture 4: Subsampling for supervised transformations

Lecture 5: Out of core text classification with the Hashing Vectorizer

Lecture 6: Summary

Chapter 10: Course Summary

Lecture 1: Course Summary

Chapter 11: Code Files

Lecture 1: Working Files

Lecture 2: Thank You!

Instructors

Eduero Academy, Inc.
Learn Web Development, AI and Data Science

Rating Distribution

1 stars: 4 votes
2 stars: 3 votes
3 stars: 7 votes
4 stars: 13 votes
5 stars: 14 votes

Frequently Asked Questions

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